Mirco Fabian Woidelko

M.Sc., M.A.
Research Assistant

Contact

Mirco Woidelko, M.Sc., M.A.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C2 1.013
Phone: +49 40 42878 4093
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Research Project

DisrupSys
Disruptive functions and technology for angle-based integrated grid operation in converter-dominated power systems with predominantly renewable energy supply

DisrupSys

Disruptive functions and technology for angle-based integrated grid operation in converter-dominated power systems with predominantly renewable energy supply

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2021 to 2024

Publications

TUHH Open Research (TORE)

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning in Electromagnetic Compatibility (EMC) Engineering (VL)
Subtitle:
This course is part of the module: Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3006_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Dr. Cheng Yang
Description:

Electromagnetic Compatibility (EMC) Engineering dealswith design, simulation, measurement, and certification of electronic andelectric components and systems in such a way that their operation is safe,reliable, and efficient in any possible application. Safety is herebyunderstood as safe with respect to parasitic effects of electromagnetic fieldson humans as well as on the operation of other components and systems nearby.Examples for components and systems range from the wiring in aircraft and shipsto high-speed interconnects in server systems and wirless interfaces for brainimplants. In this part of the course we will give an introduction to thephysical basics of EMC engineering and then show how methods of MachineLearning (ML) can be applied to expand todays physcis-based approaches in EMCEngineering.

Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS credit points:
1
Stud.IP informationen about this course:
Home institute: Institut für Theoretische Elektrotechnik (E-18)
Registered participants in Stud.IP: 2

Supervised Theses

ongoing
completed

2023

  • Babendererde, A. (2023). Regelung eines Umrichters zum Anschluss eines Wasserstoffspeicherkraftwerks an die Höchstspannungsebene.

2022

  • Lim, I. (2022). Modelling and Integration of a Hydrogen Storage Power Plant in the 10-Machine New-England Power System.

  • Lindner, J. (2022). Primärregelungskonzepte für einen Batteriepufferspeicher eines Wasserstoffspeicherkraftwerkes.

  • Rieckborn, N. (2022). Modellierung des Umwandlungsprozesses eines Wasserstoffspeicherkraftwerks.